Adoption, usability and perceived clinical value of a UK AI clinical reference platform: a mixed-methods formative evaluation of real-world usage and a 1,223-respondent user survey. (Preprint)

BACKGROUND The exponential growth of biomedical literature and clinical guidelines contributes to information overload for healthcare professionals, exacerbating clinician burnout and impeding evidence-based practice. Traditional resources often fail to deliver rapid, accessible information at the point of care. Retrieval-augmented generation (RAG) architectures using large language models (LLMs) offer potential for efficient, provenance-bound clinical decision support, but require evaluation in real-world settings to ensure safety and utility, particularly in public health informatics contexts like UK healthcare. OBJECTIVE To describe the design of iatroX, a UK-centred RAG-based clinical reference platform, and report early real-world adoption, usability, and perceived clinical utility from a formative implementation evaluation. METHODS This mixed-methods formative evaluation combined (i) a retrospective observational analysis of platform usage data across web, iOS, and Android applications over 16 weeks (8 April–31 July 2025) and (ii) a cross-sectional, in-product intercept survey. Usage metrics (unique users, engagement events, clinical queries) were sourced from Google Analytics 4, Apple App Store Connect, and Google Play Console, with bot filtering applied. A client-side script randomized single-item survey prompts to ~10% of web sessions from a predefined battery assessing usefulness, reliability, and adoption intent; responses were anonymous ("Yes/No/Don't Know"). Proportions were reported with Wilson 95% confidence intervals; qualitative comments underwent thematic content analysis. Reporting adhered to STROBE and CHERRIES guidelines. This was classified as a UK service evaluation, requiring no ethics review. RESULTS The platform achieved rapid adoption with 19,269 unique web users, 202,660 engagement events (~10.5 per active user), and ~40,000 clinical queries across platforms. Mobile downloads included 1,960 iOS and steady Android growth (peak >750 daily active users). The survey elicited 1,223 item-level responses: perceived usefulness 86.2% (95% CI 74.8–93.9%; 50/58); time saved 60.9% (95% CI 38.5–80.3%; 14/23); would use again 93.3% (95% CI 68.1–99.8%; 14/15); willingness to recommend 88.4% (95% CI 75.1–95.9%; 38/43); perceived accuracy 75.0% (95% CI 58.8–87.3%; 30/40); perceived reliability 79.4% (95% CI 62.1–91.3%; 27/34). Themes included speed, guideline-grounded responses, and UK specificity. CONCLUSIONS iatroX demonstrated strong early uptake and positive user perceptions among UK clinicians, highlighting RAG's potential to mitigate information overload in public health informatics. Limitations include small per-item sample sizes and early-adopter bias; future work should include objective accuracy audits and prospective impact studies on workflows and care quality.